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JASON on “Compressive Sensing” for DoD Sensors

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The latest report from the elite JASON science advisory panel is devoted to the subject of “compressive sensing.” This term generally refers to the use of sensors for imaging (or other sensing) of an object in a manner that uses a limited subset of the available data in order to improve efficiency or conserve resources.

“Compressive sensing involves intentionally under-sampling an object or image, typically in a random manner, and then using a companion process known as sparse reconstruction to recover the complete object or image information…,” the JASON report says.

“Compressed sensing can conceivably lead to reductions in data link requirements, reductions in radar resources needed for radar image formation (thereby providing the radar more resources for its other functions such as target detection, target tracking, and fire control), increased angular resolution without commensurate increases in array costs, and increased fields of view without degradation in resolution…”

“Compressive sensing is not a ‘free lunch’,” the report cautions, “but always involves a tradeoff; reduced data may save measurement resources, but it also means a lower signal-to-noise ratio and possibly other artifacts, such as side lobes or false alarms.”